What Actually Determines Speech-to-Text Accuracy in Meetings
Meeting transcription accuracy is usually discussed as a property of the AI model — but in practice, the same model can produce a 98%-accurate transcript of one meeting and an 85% transcript of another. The difference is almost entirely in the audio and the speaking conditions, most of which teams can influence.
The factors, ranked by impact
- Microphone and distance: a headset mic 3cm from the mouth outperforms a laptop mic at 60cm more than any model upgrade. Room echo and keyboard clatter degrade recognition disproportionately.
- Crosstalk: simultaneous speakers are the hardest problem in the pipeline — overlapping speech gets fragments attributed to the wrong person or dropped. A light 'one speaker at a time' norm measurably improves transcripts.
- Accents and language mixing: modern models handle diverse English accents far better than the previous generation, but mid-sentence language switching remains hard. Locking the transcription language when a meeting is monolingual prevents the model from hallucinating language switches.
- Domain vocabulary: product names, acronyms, and technical jargon are where most visible errors concentrate — the model substitutes a phonetically similar common word.
- Speaking rate and mumble: fast, trailing-off, or filler-dense speech degrades recognition for the same reason it degrades human comprehension.
Native audio versus bot audio
Architecture matters too: a transcription service that receives the platform's native per-speaker audio streams starts with cleaner input than a notetaker bot that joins as a participant and records the mixed room feed. Per-speaker streams also make diarization — knowing who said what — nearly exact rather than inferred. This is one of the structural advantages of platform-native transcription like MeetOye's Oya over bolt-on bots.
What accuracy is enough
For the recap use case — decisions, action items, summaries — accuracy above roughly 90-95% is functionally sufficient, because the summarization layer is robust to scattered word errors. For verbatim use cases — legal records, compliance, quotable minutes — every error matters and human review remains part of the workflow. Match the tolerance to the use, and spend your improvement effort on microphones and meeting norms before blaming the model.